Autoignition of isolated <i>n</i>-heptane droplets in air and hot combustion products at microturbine conditions
Bibliographic record
Abstract
Spontaneous ignition of isolated n-heptane droplets with initial diameters of 20–100 μm is simulated using air at 4 atm and 700–1200 K, which includes the typical operating conditions of recuperated microturbines. Because some fuel droplets in a combustor may be sprayed or carried to near the recirculation zone, the simulations use a mixture of pure air and hot combustion products as the oxidiser. The flame structures, evaporation times, and autoignition times in both physical and mixture fraction spaces for the different conditions are presented and compared. The variables examined include the air preheat temperature, amount of dilution with hot products, initial fuel droplet diameter, oxidiser temperature, and oxygen concentration. The results show that droplets in pure air at microturbine conditions fully evaporate before ignition, suggesting that a prevaporised concept is suitable for microturbines. The dilution with hot combustion products decreases the ignition delay time mainly by raising the oxidiser temperature. Low-temperature chemistry does not have a significant effect on droplet ignition because adding even a small amount of hot combustion products can increase the oxidiser temperature to higher than the temperatures favourable for low-temperature kinetics. The cool flame is only observed for 100 μm droplets at low temperatures, but two-stage ignition is not observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".